People as Sensors: Imputing Maps from Human Actions

Abstract

Despite growing attention in autonomy, there are still many open problems, including how autonomous vehicles will interact and communicate with other agents, such as human drivers and pedestrians. Unlike most approaches that focus on pedestrian detection and planning for collision avoidance, this paper considers modeling the interaction between human drivers and pedestrians and how it might influence map estimation, as a proxy for detection. We take a mapping inspired approach and incorporate people as sensors into mapping frameworks. By taking advantage of other agents' actions, we demonstrate how we can impute portions of the map that would otherwise be occluded. We evaluate our framework in human driving experiments and on real-world data, using occupancy grids and landmark-based mapping approaches. Our approach significantly improves overall environment awareness and out-performs standard mapping techniques.

7 pages, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

References in corpus (2)

• Joint Attention in Autonomous Driving (JAAD)
• Evaluation of Automated Vehicles Encountering Pedestrians at Unsignalized Crossings

Cited by in corpus (7)

• Social Interactions for Autonomous Driving: A Review and Perspectives
• Deep Learning for Vision-based Prediction: A Survey
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• Inferring Occluded Agent Behavior in Dynamic Games from Noise Corrupted Observations
• Behavior Planning of Autonomous Cars with Social Perception
• Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments
• Interaction-Aware Behavior Planning for Autonomous Vehicles Validated with Real Traffic Data